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, patch clamp recording, confocal microscopy, calcium imaging and other biochemical analysis (western blot, ELISA, etc.). Ability to work independently and collaboratively in a team environment Excellent
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: Three Casual Research Assistant 2 positions are open at the Montreal Brain Imaging Centre of the Neurological Institute. The candidates will develop pipelines for classification of patients and healthy
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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/nucleus RNAseq and ATACseq, human iPSCs, Serial Block face EM, mouse genetics, biochemistry, cell signaling control, in vivo 2-photon live imaging, and translational pharmacology to peel back the layers
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. Experience writing project proposals. Experience in tomography analysis for 3-D imaging. * Significant experience is defined as two (2) to five (5) years of experience. Condition of Employment Reliability
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MSc/PhD Position at the Faculty of Medicine, Memorial University of Newfoundland, St. John’s, Canada
-of-the-art medical laboratories equipped with high-resolution confocal imaging, a Seahorse analyzer for metabolic profiling, flow sorters and cytometers, an electron microscopy suite with advanced tissue
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various photo-activated material using advanced techniques such as SEM-EDX, TEM, TGA, XRD, IR spectroscopy, and BET. Prototype Development: Design and develop UV-activated gas sensor prototypes, including
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-EDX, TEM, TGA, XRD, IR spectroscopy, and BET. Prototype Development: Design and develop UV-activated gas sensor prototypes, including hardware design, sourcing components, and assembling sensors
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with advanced biochemistry, genetic, and imaging techniques Who Should Apply? We welcome applicants with a strong background in molecular biology, neuroscience, biochemistry, or genetics. Eligible
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thermoregulation from leaf to landscape Jonathan Doucette Doctor of Philosophy in Physics (PhD) Probing Brain Tissue Microstructure with Magnetic Resonance Imaging through Bayesian Learning of Signal Dynamics Megan